Computational Chemistry Co-op , Closed-Loop Ligand Design and Optimization (Doctoral)
Core
Developing a closed-loop computational and experimental platform to accelerate chiral ligand development and optimization for synthetic drug substances.
Role type
Doctoral co-op computational chemist (ligand design)
Builds
Closed-loop pipeline integrating DFT, machine learning, and Bayesian optimization for catalyst design
Domain
Pharmaceutical / Computational Chemistry / Asymmetric Catalysis
Deliverable
production ML models | product features
Required skills
DFT calculations, transition-state theory, synthetic organic chemistry, Python programming, machine learning fundamentals, organic stereochemistry, asymmetric catalysis
Preferred skills
Cheminformatics, Bayesian optimization, active learning, molecular descriptor generation, feature attribution
Technologies
DFT, Python, Bayesian optimization frameworks
Responsibilities
Partner with synthetic chemists to identify selectivity challenges and co-design training sets; Perform DFT transition-state calculations for chiral ligand libraries; Develop ML surrogate models and implement Bayesian optimization campaigns; Test computational predictions experimentally and refine design cycles; Build and maintain the closed-loop computational pipeline; Communicate technical approaches and results.
Seniority
Doctoral student (Co-op)
